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SmartChain : A Warehouse Optimization

PickOptima is a powerful simulation tool designed to analyze and optimize order-picking efficiency based on wave size. By providing adjustable parameters and visual insights, this tool helps businesses streamline their picking process to enhance operational productivity.

Project Structure

PickOptima/
├── app.py  # Main application for visualization using Streamlit
├── utils/
│   ├── simulation/
│   │   ├── simulation_batch.py  # Simulates batch picking processes
│   └── batch/
│       ├── mapping_batch.py  # Handles mapping logic for orders
└── myenv/  # Virtual environment (not included in GitHub)

Features

  • Dynamic Order Line Simulation:

    • Adjust the number of order lines for analysis.
    • Simulate the impact of varying wave sizes on picking efficiency.

  • Interactive Visualizations:

    • Provides graphs and charts to visualize the performance metrics.

  • Customizable Parameters:

    • Configure the simulation with minimum and maximum wave sizes.

  • Scalable Design:

    • Modular architecture for easy customization and extension.

Installation Guide

1. Prerequisites

  • Python 3.8+
  • Git

2. Clone the Repository

git clone https://github.com/MalyajNailwal/PickOptima.git
cd PickOptima

3. Set Up Virtual Environment

Create and activate a virtual environment:

python3 -m venv myenv
source myenv/bin/activate  # macOS/Linux
myenv\Scripts\activate  # Windows

4. Install Dependencies

Install required libraries:

pip install -r requirements.txt

How to Run the Application

  1. Activate the virtual environment (if not already active).

    source myenv/bin/activate  # macOS/Linux
myenv\Scripts\activate  # Windows

  2. Launch the Streamlit app:

    streamlit run app.py

  3. Open the provided URL in your web browser to access the application.

Usage

1. Simulation Inputs

  • Order Line Scope: Adjust the total number of order lines for analysis.
  • Wave Size Range: Configure the minimum and maximum wave sizes.

2. Outputs

  • Visualizations showcasing the relationship between wave size and picking efficiency.
  • Insights to optimize order-picking workflows.

File Descriptions

1. app.py

The main Streamlit application that:

  • Collects user inputs.
  • Displays interactive graphs and outputs.
  • Integrates functionalities from utility modules.

2. simulation_batch.py

Simulates batch order-picking processes based on user-defined parameters. Key methods include:

  • simulate_batch: Generates simulation data.

3. mapping_batch.py

Handles mapping logic for orders, including:

  • Generating mapping data for simulation.

Technologies Used

  • Programming Language: Python 3.8+
  • Framework: Streamlit
  • Data Visualization: Matplotlib, Plotly

Contributing

Contributions are welcome! To contribute:

  1. Fork the repository.
  2. Create a new branch: 
    git checkout -b feature-name
  3. Commit your changes: 
    git commit -m "Add feature-name"
  4. Push to the branch: 
    git push origin feature-name
  5. Open a pull request.